This book is designed to help preservice and inservice teachers learn about some of theeducational implications of current uses of Artificial Intelligence as an aid to solving problemsand accomplishing tasks. Humans and their predecessors have developed a wide range of tools tohelp solve the types of problems that theyface. Such tools embody some of the knowledge andskills of those who discover, invent, design, and build the tools. Because of this, in some sense atool user gains in knowledge and skill by learning to make use of tools.

This document uses the term “tool” in a very broad sense. It includes the stone ax, the flintknife, reading and writing, arithmetic and other math, the hoe and plough, the telescope,microscope, and other scientific instruments, the steam engine and steam locomotive, thebicycle, the internal combustion engine and automobile, and so on. It also includes the computerhardware, software, and connectivity that welump together under the title Information andCommunication Technology(ICT).

Artificial intelligence(AI) is a branch of the field of computer and information science. Itfocuses on developing hardware and software systems that solve problems and accomplish tasksthat—if accomplished by humans—would be considered a display of intelligence. The field ofAI includes studying and developing machines such as robots, automatic pilots for airplanes andspace ships, and “smart” military weapons. Europeans tend to use the term machine intelligence(MI) instead of the term AI.

The theory and practice of AI is leading to the development of a wide range of artificiallyintelligent tools. These tools, sometimes working under the guidance of a human and sometimeswithout external guidance, are able to solve or help solve a steadily increasing range ofproblems. Over the past 50 years, AI has produced a number of results that are important tostudents, teachers, our overall educational system, and to our society.

This short book provides an overview of AI from K-12 education and teacher educationpoints of view. It is designed specifically for preservice and inservice teachers and schooladministrators. However, educational aides, parents, school site council members, school boardmembers, and others who are interested in education will find this booklet to be useful.

This book is designed for self-study, for use in workshops, for use in a short course, and foruse as a unit of study in a longer course on ICT ineducation. It contains a number of ideas forimmediate application of the content, and it contains a number of activities for use in workshopsand courses. An appendix contains suggestions for Project-Based Learning activities suitable foreducators and students.

3

Chapter 1: Intelligence and Other Aids to Problem Solving

This short book is about how humans are using artificial intelligence (AI; also known asmachine intelligence)as an aid to solving problems and accomplishing tasks. The book placesspecific emphasis on educational applications and implications of AI.

This first chapter provides background needed in the remainder of the book. The backgroundincludes:

•

Several definitions of artificial intelligence.

•

A discussion of human intelligence.

•

A brief introduction to problem solving.

What is Artificial Intelligence?

There is ahuge amount of published research and popular literature in the field of AI(Artificial Intelligence-a & b, n.d.; Minsky 1960; AI Journals & Associations, n.d.).JohnMcCarthy coined the phrase Artificial Intelligence as the topic of a 1956 conference held atDartmouth (Buchanan, n.d.).

Here are three definitions of AI. The first is from Marvin Minsky, a pioneer in the field. Thesecond is from Allen Newell, a contemporary of Marvin Minsky. The third is a more modern,1990 definition, and it is quite similar to the earlier definitions.

In the early 1960s Marvin Minsky indicated that “artificial intelligence is the science of makingmachines do things that would require intelligence if done by men.” Feigenbaum and Feldman(1963) contains substantial material written by Minsky, including “Steps Toward ArtificialIntelligence” (pp 406-450) and “A Selected Descriptor: Indexed Bibliography to the Literature onArtificial Intelligence” (pp 453-475)

In Unified Theories of Cognition, Allen Newell defines intelligence as: the degree to which asystem approximates a knowledge-level system. Perfect intelligence is defined as the ability tobring all the knowledge a system has at its disposal to bear in the solution of a problem (which issynonymous with goal achievement). This may be distinguished from ignorance, a lack ofknowledge about a given problem space.

Artificial Intelligence, in light of this definition of intelligence, is simply the application ofartificial ornon-naturally occurring systems that use the knowledge-level to achieve goals.(Theories and Hypotheses.)

What is artificial intelligence? It is often difficult to construct a definition of a discipline that issatisfying to all of its practitioners. AI research encompasses a spectrum of related topics. Broadly,AI is thecomputer-basedexploration of methods for solving challenging tasks that havetraditionally depended on people for solution. Such tasks include complex logical inference,diagnosis, visual recognition, comprehension of natural language, game playing, explanation, andplanning (Horvitz, 1990).

In brief summary, AI is concerned with developing computer systems that can storeknowledge and effectively use the knowledgeto help solve problems and accomplish tasks. Thisbrief statement sounds a lot like one of the commonly accepted goals in the education of humans.We want students to learn (gain knowledge) and to learn to use this knowledge to help solveproblems and accomplish tasks. Goals of education are discussed in chapter 2 of this book.

You may have noticed that the definitions of AI do not talk about the computer’s possiblesources of knowledge. Two common sources of an AI system’s knowledge are:

4

•

Human knowledgethat has been converted into a format suitable for use by an AIsystem.

•

Knowledge generated by an AI system, perhaps by gathering data and information, andby analyzing data, information, and knowledge at its disposal.

While most people seem to acceptthe first point as being rather obvious, many view the secondpoint only as a product of science fiction. Many people find it scary to think of a machine that insome sense “thinks” and thereby gains increased knowledge and capabilities. However, this is animportant aspect of AI. We will discuss it more in chapter 7.

The Web has a type of intelligence and learning capability. The sense of direction of Webdevelopers is to make the Web more intelligent—to create a Semantic Web. Tim Berners-Lee,the inventor of the Web, is leading this endeavor. (Seehttp://www.w3.org/People/Berners-Lee/.)The underlying idea is that each person adding content to the Web is helping to increase the

knowledge of the Web (Gibson, 2005).

What is Human Intelligence?

The study and measurement of intelligence have long histories. For example, Alfred Benet

and Theodore Simondeveloped the first Intelligence Quotient (IQ) test in the early 1900s.Chances are, you have taken several IQ tests, and perhaps you can name a number that was yourscore on one of these tests. Likely, you feel it is very strange to think that a single number is auseful measure of a person’s cognitive abilities. Many people argue that a person has multipleintelligences, and that no single number is an adequate representation of a person’s intelligence.

IQ is a complex concept. There is no clear agreement among IQ experts as to whatconstitutes IQ or how to measure it. (Most people are not satisfied by the statement “IQ is whatis measured by an IQ test.”)

Howard Gardner (1993), David Perkins (1995), and Robert Sternberg (1988) are researcherswho have written widely sold books about intelligence. Of these three, Howard Gardner isprobably best known by K-12 educators. His theory of Multiple Intelligenceshas proven quitepopular with such educators (Mckenzie, n.d.). However, there are many researchers who havecontributed to the extensive and continually growing collection of research papers on intelligence(Yekovich 1994).

The following definition of intelligence is a composite from various authors, especiallyGardner, Perkins, and Sternberg.

Intelligence is a combination of the abilities to:

1.

Learn. This includes all kinds of informal and formal learning via any combination ofexperience, education, and training.

Solve problems. This includes solving problems, accomplishing tasks, and fashioningproducts.

There is a near universal agreement among researchers that some aspects of our intellectualabilities depend heavily on our experiential histories, and some aspects depend on our geneticmakeup. Thus, a person’s cognitive abilities are a combination of nature and nurture.

5

From a teacher’s point of view, it is important to understand that a person’s lifeexperiences—which include formal and informal education—contribute to the person’sintelligence. Education is very important!

We know that we can improve a child’s developing intelligence by avoiding drug and alcoholdamage to the fetus, by providing appropriate vitamins, minerals, and nutrition to support growthof a healthy mind and body, and by protecting the child from the lead that used to be a commoningredient of paint and leaded gasoline.

The above definition and discussion focuses on cognitive intelligence. Emotional intelligenceis also a type of intelligence that is important in the study of AI (Mendiratta, n.d.).The idea ofemotional intelligence (EI) has been developed over the past two decades (Hein). Quoting SteveHein:

Here I will discuss only the definition of emotional intelligence as proposed by Mayer, Saloveyand their recent colleague David Caruso. (Referred to below as MSC.)

MSC suggest that EI is a true form of intelligence which has notbeen scientifically measured untilthey began their research work. One definition they propose is "the ability to process emotionalinformation, particularly as it involves the perception, assimilation, understanding, andmanagement of emotion." (Mayer and Cobb, 2000)

Elsewhere they go into more detail, explaining that it consists of these "four branches of mentalability":

1.

Emotional identification, perception and expression. This involves such abilities asidentifying emotions in faces, music, and stories.

2.

Emotional facilitation of thought. This involves such abilities as relating emotions to othermental sensations such as taste and color (relations that might be employed in artwork),and using emotion in reasoning and problem solving.

3.

Emotionalunderstanding. This involves solving emotional problems such as knowingwhich emotions are similar, or opposites, and what relations they convey

4.

Emotional management. This involves understanding the implications of social acts onemotions and the regulation of emotion in self and others.

Some AI researchers are working in the area of EI. At the current time, humans are farsuperior to computers in terms of EI performance.

Some of Marvin Minsky’s insights into human and machine intelligence are provided in a1998 interview (Sabbatini, 1998). This interview helps to flesh out the definitions given above.Quoting the first part of the interview:

Sabbatini: Prof. Minsky, in your view, what is the contribution that computer sciences canmake to the study of the brain and the mind?

Minsky: Well, it is clear to me that computer sciences will change our lives, but not because it’sabout computers. It’s because it will help us to understand our own brains, to learn what is thenature of knowledge. It will teach us how we learn to think and feel. This knowledge will changeour views of Humanity and enable us to change ourselves.

Sabbatini: Why are computers so stupid?

Minsky: A vast amount of information lies within our reach. But no present-day machine yetknows enough to answer the simplest questions about daily life, such as:

"You should not move people by pushing them."

"If you steal something, the owner will be angry."

"You can push things with a straight stick but not pull them."

"When you release a thing [you are] holding in your hand it will fall toward earth (unless it is ahelium balloon)."

6

"You cannot move an object by asking it "please come here."

No computer knows such things, but every normal child does.

There are many other examples. Robots make cars in factories, but no robot can make a bed, orclean your house or baby-sit. Robots can solve differential equations, but no robot can understanda first grade child’s story. Robots can beat people at chess, but no robot can fill your glass.

We need common-sense knowledge—and programs that can use it. Common sense computingneeds several ways of representing knowledge. It is harder to make a computer housekeeper than acomputer chess-player, because the housekeeper must deal with a wider range of situations.

A brief summary of the history of AI is given in Kurzweil (1991). He uses the term machineintelligence to refer to the general field of AI. Kurzweil has made many important contributionsto the field. For example, many years ago he developed a text to speech machine for the blind.

An Introduction to Problem Solving

This section contains a very brief introduction to problem solving. A more detailedintroduction is available in Moursund (2004).

The termsproblemandproblem solvingare used throughout this document. We use theseterms in a very broad sense, so that they include:

•

posing, clarifying, and answering questions

•

posing, clarifying, and solving problems

•

posing, clarifying, and accomplishing tasks

•

posing, clarifying, and making decisions

•

using higher-order, critical, and wise thinking to do all of the above

Problem solving consists of movingfrom a given initial situation to a desired goal situation.That is, problem solving is the process of designing and carrying out a set of steps to reach agoal. Figure 1.1 graphically represents the concept of problem solving. Usually the termproblem

isused to refer to a situation where it is not immediately obvious how to reach the goal. Theexact same situation can be a problem for one person and not a problem (perhaps just a simpleactivity or routine exercise) for another person.

There is a substantial amount of research literature as well as many practitioner books onproblem solving (Moursund, 2004). Here is a formal definition of the term problem. You—personally—have a formal, well-defined (clearly defined) problem if the following fourconditions are satisfied:

1.

You have a clearly defined given initial situation.

7

2.

You have a clearly defined goal (a desired end situation). Some writers talk abouthaving multiple goals in a problem. However, such a multiple goal situation can bebroken down into a number of single goal problems.

3.

You have a clearly defined set of resources—including your personal knowledge andskills—that may be applicable in helping you move from the given initial situation tothe desired goal situation.There may be specified limitations on resources, such asrules, regulations, and guidelines for what you are allowed to do in attempting to solvea particular problem.

4.

You have some ownership—you are committed to using some of your own resources,such as your knowledge, skills, and energies, to achieve the desired final goal.

The resources (part 3 in the definition) available to a person certainly include their mind andbody. A carpenter typically has a wide range of hand and power tools, along with acquiredknowledge and skill in how to use the tools. In this book, we are particularly interested in ICT—especially, AI—as one of the resources in problem solving. ICT systems can solve or help solvea number of problems of interest to humans. From an educational point of view, this raises twoquestions:

•

If a computer can solve or substantially aid in solving a type of problem that students arestudying in school, what should students be learning about solving this type of problem?(For example, should theybe learning to compete with computers or work cooperativelywith computers?)

•

Are there topics that should be eliminated from the curriculum or topics that should beadded to the curriculum because of the capabilities of computers to solve problems and/orto assist in solving problems?

Think about these questions as you read this book. As a reader, one of your goals should be toform well-reasoned answers for yourself. In addition, you should pose other, equally complexquestions that are of interest to you and others.

Key Ideas in This Chapter

The following diagram helps to summarize some of the ideas of this chapter.

At the center of the diagram is a team consisting ofone or more people working to solve aproblem or accomplish a task. The team makes use of tools that extend their mental capabilities(such as reading, writing, arithmetic, calculators, and computers) and tools that extend theirphysical capabilities (suchas a carpenter’s tools, cars, and airplanes). The team has hadeducation and training in using available resources to solve problems and accomplish tasks. Theoverall capabilities of the team are improved by providing the team with better tools, bettereducation, better training, and additional experience.

Over the centuries, humans have made substantial progress in producing tools to supplementtheir physical capabilities. People routinely use eyeglasses, binoculars, telescopes, andmicroscopes to augment and extend their eyesight. People routinely use bulldozers and trucks toaugment and extend their muscle power. However, we do not use the termsartificial eye,artificial body,orartificial muscleto

describe the theory and practice of developing and usingsuch tools. For the most part, people do not debate whether artificial muscle is as good or betterthan “real, human” muscle. They do not think that a school that teaches people to drive largetrucksor bulldozers is inherently suspect, and that it would be better if such schools taught thebasics of moving goods and dirt by hand.

In retrospect, John McCarthy’s 1956 choice of the termartificial intelligencemay have donea disservice to the field. For many people, the term AI tends to be an emotion-laden term that issuggestive of developing Frankenstein-like monsters that will replace humans.

This book explores the capabilities and limitations of ICT systems to process and use data,information, knowledge, and wisdom to help automate cognitive tasks. It also explores the use ofsuch ICT in machines such as robots. Throughout this book we will use the term AI, althoughfrom time to time we will use the termmachine intelligenceto help stress that we are talkingabout something that is quite different than human intelligence.

9

Personal Growth Activities for Chapter 1

Each section of this document contains one or moresuggestions for reflection and possibleconversations based on the ideas covered in the section. The intent is to get you actively engagedin learning and using the materials that you are reading.

1.

Engage some of your colleagues in a conversation about cognitive intelligence andemotional intelligence. Your goal is to explore your insights and your colleagues’insights, especially as they apply to students. After you have practiced talking aboutcognitive intelligence and emotional intelligence, engage some of your students in aconversation about these topics. Your goal is to gain increased insight into how yourstudents view and understand these topics and how they relate to schooling.

2.

Think about “intelligent-like” things that you have seen machines do. For example,perhaps you have seen talking toys that respond to a child. Perhaps you have used acomputer that displays some intelligent-like behaviors. Talk to someone (a friend, achild, etc.) about the nature of the machine intelligence that you have observed andthat they have observed. Focus on the capabilities and limitations that the two of youhave seen, and how this machine intelligence has affected your worlds. It isparticularly helpfulto talk to primary school children on this topic. A child’s view ofmachine intelligence may be quite a bit different from yours. If this topic interestsyou, visit Sherry Turkle’s Website (Turkle, n.d.). She has spent most of herprofessional career studying computers from a child’s point of view.

Activities for Chapter 1

Activities are for use in reflection and self-study, for use in workshops and small groupdiscussions, and for use as written assignments in courses. In almost all cases the Activities focuson higher-order “critical thinking” ideas.

1.

Think about a shovel. A person using a shovel may well be able to accomplish adigging task faster and with less effort than a person who does not have access to thetool. Discuss how a shovel in some sensecontains or embodies some of theknowledge and skills of its inventors, developers, and manufacturers. Does this meanthat in some sense a shovel has some level of machine intelligence?

2.

Think about an electronic digital watch. Analyze it from the pointof view of itscapabilities and limitations in problem solving. In what sense is an electronic digitalwatch “intelligent?” As you respond to this question, include an analysis of thismachine intelligence versus human intelligence within the area of thespecific problemsthat the watch is designed to help solve.

3.

Briefly summarize how reading, writing, and arithmetic are mind tools that extend thecapabilities of the human mind. Then reflect on whether having knowledge and skillsin reading, writing, and arithmetic makes a person more intelligent. As you addressthis task, you are delving into the deep area of “What is intelligence?” From yourpoint of view, what does the wordintelligencemean?

4.

Consider the definitions of intelligence and emotionalintelligence given in this chapter.In your personal opinion, how should our educational system take into considerationthe widely differing (cognitive) intelligence and emotional intelligence of students?

10

5.

Select a subject area that you teach or are preparing to teach. Name a general type ofproblem that students learn to solve because of instruction in this area. Make surethat the general type of problem you name satisfies the first three parts of thedefinition of a formal problem given in this chapter. Then discuss the “ownership”part of the definition from the point of view of students. If students lack personalownership in the types of problems they are learning to solve, how does this affecttheir intrinsic and extrinsic motivation?

11

Chapter 2: Goals of Education

One of the main goals of this book is to explore the current and potential impact of AI on oureducational system. Will (and/or should) AI have a significant impact onour educational goalsand objectives? This chapter discusses general goals of education, and it provides backgroundneeded as we explore applications of AI that are related to these goals.

Three General Goals of Education

Each person has their own ideas onwhat constitutes appropriate goals for education. Thus,this topic can lead to heated debate and is currently a major political issue. Curriculum content,instructional processes, and assessment are all controversial issues. What constitutes a “good”education or a “good” school?

David Perkins' 1992 book contains an excellent overview of education and a wide variety ofattempts to improve our educational system. He analyzes these attempted improvements in termsof how well they have contributed to accomplishing the following three major goals of education(Perkins, 1992, p5):

1.

Acquisition and retention of knowledge and skills.

2.

Understanding of one's acquired knowledge and skills.

3.

Active use of one's acquired knowledge and skills. (Transfer of learning. Ability toapply one's learning to new settings. Ability to analyze and solve novel problems.)

These three general goals—acquisition & retention,understanding, and use of knowledge &skills—help guide formal educational systems throughout the world. They are widely acceptedgoals that have endured over the years. They provide a solid starting point for the analysis of anyexisting or proposed educational system. We want students to have a great deal of learning andapplication experience—both in school and outside of school—in each of these three goal areas.

All three goals use the termknowledge and skills.Later in this chapter we will take a closerlook at the terms data, information, knowledge, and wisdom. For now, it suffices to think of thetermknowledgeas encompassing the full range of data, information, knowledge, and wisdom.The termskillsis taken to mean both physical skills and mental skills. Thus, the termknowledgeand skillsis intended to encompass the full range of physical and mental development.

You will notice that Perkins’ three goals do not speak to the specifics of curriculum content,instructional processes, student assessment, teacher education, and other major—oftencontroversial—issues in education. The generality of the three goals makes them quite useful indiscussions about Information and Communication Technology and other potential changeagents in education. However, remember, “the devil is in the details.”

The next three sections expand on the three goals stated by Perkins. These sections capturethe essence of changes that Perkins, your author, and many others feel are needed in oureducational system.

Education Goal # 1: Acquisition and Retention

Much of our current educational system can be described as “memorize, regurgitate, andforget.” Students learn to “study for the test.” Often the test is one in which memorization andregurgitation works well. However, the human mind has a strong propensity to forget memorizedinformation that it does not understand and that it does not frequently use. Thus, most of what is

12

memorized for a test is quickly forgotten. The retention part of goal 1 is not well served by thisapproach to learning.

There is another difficulty with a rote memorization approach to learning. The totality ofaccumulated knowledge is increasing exponentially. Estimates of the doubling time vary, withsome people suggesting a doubling of every 5 or 10 years, and some suggesting an even shorterdoubling time. The increase in the total accumulated knowledge of the human race in just oneweek is far more than a person can memorize in a lifetime.

A somewhat similar analysis holds for skills that one might acquire. It takes a long period ofstudy and practice to become reasonably skilled at archery, art, basketball, bowling, crocheting,cursive handwriting, dancing, drawing, fast keyboarding, guitar playing, piano playing, and soon. That is, there are many different areas in which, through study and practice, a person cangain a personally useful level of knowledge and skills. Nobody has the time to become highlyskilled in every skill area.

Computers are very good in storage, retention, and regurgitation. When it comes to rotememory and retention, computers are far superior to humans. If one considers the types of skillsthat can be automated by computerized tools, then computers have the capability to acquire agreat many different skills. Computer systems gain new skills through the development of newhardware and software.

Education Goal # 2: Understanding

In talking about understanding, it is helpful to consider the “scale” pictured below.

DataInformationKnowledgeWisdomMovingtowardincreased"understanding."

Figure 2.1. Data, Information, Knowledge, Wisdom, and Understanding

The following quotation provides definitions of the terms data, information, knowledge, andwisdom in the specific context of biology (Atlantic Canada Conservation Data Centre; n.d.). Theideas from this specific discipline easily carry over to other fields.

Individual bits or "bytes" of "raw" biologicaldata(e.g. the number of individual plants of a givenspecies at a given location) do not by themselves inform the human mind. However, drawingvarious data together within an appropriate context yieldsinformationthat may be useful (e.g. thedistribution and abundance of the plant species at various points in space and time). In turn, thisinformation helps foster the quality ofknowing(e.g. whether the plant species is increasing ordecreasing in distribution and abundance over space and time). Knowledge and experience blendto becomewisdom--the power of applying these attributes critically or practically to makedecisions.

A computeris a machine designed for the input, storage, manipulation, and output of dataand information.It is clear that a computer system can store and process data and information.But, what about knowledge and wisdom? An electronic digital watch displays the time and date.

13

However, the watch has no understanding of the meaning of time and date. Knowledge andwisdom require understanding, not just rote memory.

One approach to thinking about possible meanings ofknowledgeis to consider uses that canbe made of the knowledge. For example, suppose that a building contains a number of electronicdigital thermostats that are connected to a computer that can turn on/off the heating and coolingunits in individual parts of the building. The job of this computerized heating and cooling systemis to maintain the temperature at a comfortable level in all parts of the building. This is to bedone in a cost effective manner. The system might also contain sensing devices that can tell ifpeople occupy a part of the building, and maintain lower temperatures in rooms that are notoccupied.

This computerized heating and cooling system has the knowledge and skills that are neededto solve a quite complex problem. In a large building, it can surely outperform a group ofhumans attempting to accomplish thesame task. That is, within its very narrow domain ofexpertise, the heating and cooling system has the knowledge and skills to accomplish a complextask—and can do it better than humans. You might want to refer back to the definitions of AIgiven in chapter 1 to see that this system satisfies definitions of AI. At the same time, you mightthink about whether the heating and cooling system has any “understanding” of what it is doing.

Understanding is a tricky issue. A young baby cries in response to some internal sensing ofhunger, cold, wet bedding, etc. The crying often produces a response from the caregiver, and theproblems are solved. Does the baby have an understanding of hunger, cold, wetness, and so on?

It is interesting to engage people in conversations about whether a computer can store andmake effective use of knowledge or wisdom. Perhaps knowledge and wisdom require a level ofunderstanding that is only available to human minds. Perhaps the “intelligence” of machines islimited to being able toprocess data and information somewhat in the same manner as studentsdo who pass tests using rote memorization without understanding.

A conversation about the potentials of computers storing and using knowledge becomes moreinteresting as one introduces the idea that many businesses are now actively engaged in usingcomputers for “knowledge management.” Knowledge management is about the use of computersto process data and information in order to produce knowledge (ACM SIGKDD,n.d.,Godbout,1999).

The recent development and rapid growth of the field of knowledge management suggest thatmany people feel computer systems can effectively deal with knowledge and make wisedecisions.

Education Goal # 3: Active Use

One of the major goals in education is transfer of learning from a specific classroom-learningenvironment to other environments. We want students to be able to use their school-acquiredknowledge and skills at home, at work, at play, and at school—immediately, and far intothefuture, and in varied settings.

In recent years the Science of Teaching and Learning has made significant progress(Bransford et al, 1999). New and better learning theories and transfer of learning theories havebeen developed. Computers are playing asignificant role in both the development andimplementation of these theories.

Recent research in situated learning theory indicates that much of what we learn is intricatelyintertwined with the environment or situation in which we learn it (Situated Learning Theory,

14

n.d.). Thus, the learning environment needs to be designed to be relatively similar to theenvironments in which we want students to apply their learning.

A good example of situated learning is provided by the“Help” features that are part of manycomputer applications. We want students to become more self reliant in finding answers to thetypes of problems they encounter as they use sophisticated pieces of software such as a wordprocessor. Thus, we can teachthem to use the built-in help features of the software, knowing thatsuch built-in help is available whenever and wherever they are making use of the software. Youand your students should be aware that a well-designed help feature in software represents theeffective storage of knowledge in a form that it is easy to retrieve and use by a human. Suchsystems make use of AI.

If you are a Star Trek fan, you know about the Holodeck, which is a very sophisticatedcomputerized virtual reality environment. More generally, computer simulations—includingvirtual reality—are gradually becoming useful educational and research tools. Such simulationscan engage a learner in actively using knowledge and skills that are being acquired. A virtualreality can be thought of as computer storage of data, information, and knowledge in a form thatfacilities a realistic, real-world-like interaction with a human. In this interaction, the humanmakes active use of knowledge and skills, and thehuman may well gain increased knowledgeand skill. Because of the reality of the simulation, considerable transfer of learning occurs fromuse of the simulation to applications in the real world.

The past two decades have seen substantial progress in understanding transfer of learning andhow to teach for transfer. A good example of this progress is provided by the high-road, low-road transfer theory developed by Perkins and Salomon (2002). Low-road transfer involveslearning to a high level of automaticity, rather like the stimulus-response approach of behaviorallearning theory. High-road transfer requires understanding and mindfulness. Many schools andschool districts are placing increased emphasis on teaching for understanding. Computers arenow extensively used in helping students learn certain facts (number facts, for example) to a highlevel of automaticity. A well designed “Intelligent” Computer-Assisted Learning (ICAL) systemengages the learner in interactions in which the learner is making immediate and active use ofwhat is being learned.

This diagram suggests that lower-order knowledge and skills are heavily weighted on the side ofdata, information, and a low level of understanding. Higher-order knowledge and skills areheavily weighted on the side of knowledge, wisdom, and a high level of understanding.

The following Expertise Scale is useful in discussing lower-order and higher-orderknowledge and skills. Pick any specific area in which a student begins with a very low level (a“novice” level of knowledge and skills), and then works toward acquiring a higher level ofexpertise. Think about designing and implementing a teaching/learning environment thatefficiently and effectively helps a learner to gain increased expertise in the area.

Novi ceUsefulLevelofKnowledge&Ski l l sWorldClassExpertiseScale

Figure 2.3. A general-purpose expertise scale.

At every grade level and in every subject area, student learning consists of some emphasis onlower-order knowledge and skills, and some emphasis on higher-order knowledge and skills. Insimplified terms, the “back to basics” movement is one of placing a greater emphasis on learninglower-order knowledge and skills to a high level of automaticity. The underlying learning theoryis behavioral learning theory or low-road transfer.

Other groups of educators want to tip the balance toward the higher-order knowledge andskills side of the scale. They feel that our school should provide an education that supports high-road transfer. Part of their argument is that computers and other tools can and should replacesome of the emphasis currently being placed on lower-order knowledge and skills. There is agrowing recognition that more school time needs to be spent on higher-order knowledge andskills, and less time should be spent helping students to learn to do thingsthat computers can domore quickly and accurately than people.

16

Goals of ICT is Education

Historically, the computer field has included a major emphasis ondata processing.

Relatively early on, this changed to being an emphasis ondata and information processing.

Indeed, a commonly used definition is that a computer isa machine designed for the input,storage, manipulation, output of data and information.As computers have become rathercommonplace in our society and the field of computer and information science has continued togrow, schools are faced by a triple challenge:

1.

Determining what students should learn about the field of computer and informationscience as a discipline in its own right.

2.

Determining what aspects of computer and information science can and should beintegrated into the content of the traditional curriculum areas.

3.

Determining appropriate roles of computers as an aid to teaching and learning. (Thereis steady progress in the development of highly interactive computer-assistedlearningsystems that make use of AI. This topic will be discussed more in chapter 7.)

Various professional societies have explored some or all of these issues (OTEC, n.d.). Forexample, the International Society for Technology in Education (ISTE) has developed NationalEducational Technology Standards for PreK-12 students, teachers, and school administrators(ISTE, n.d.). The National Council of Teachers of Mathematics addresses roles of calculatorsand computers in its standards documents (NCTM, n.d.).

Brittleness

AI researchers use the termbrittleto describe software that may appear to be reliable, butthat may fail badly under a variety of circumstances. The same idea can be applied to computersystems(hardware plus software) and to a person’s education. Brittleness is an important idea inboth AI and human intelligence.

You know that cells in your body die over a period of time and are replaced by other cells.Some of the neurons in your brain die overtime, and some new neurons develop. (For a longtime, brain scientists thought that no new neurons develop after birth. In recent years, thissupposition has proven to be incorrect. However, as one grows old, it is likely the rate of death ofneurons exceeds the rate of production of new neurons.)

Clearly, a human neuron and a transistor are not the same thing. If a transistor or otherelectronic component in a computer fails, this may well cause the entire computer to fail or tomake errors as it continues to function. Thus, a modern computer includes self-checkingprovisions and some provisions for dealing with flaws that are detected. For example, if acomputer disk develops a flaw, the computer system may just stop using this flawed portion ofthe disk.A computer system can be designed so that if a piece of its internal memory becomesflawed, the computer stops using this piece of memory.

However, consider another type of difficulty. As computer components such as transistorsare made smaller and smaller, the likelihood of a component making a random error increases.For example, during a computation or storage/retrieval, a bit may change from a 1 to a 0 due to arandom error in the hardware. It is possible to build hardware with enough error detection anderror correction capabilities so that such a problem may be overcome, but this is expensive andnot implemented in the types of computers than most people use.

17

One way to do this is to have three identical computers, all doing exactly the samecomputations. If all three agree on a result, this gives considerably increased confidence in thecorrectness of the computations. If two out of three agree, this is an indication that somethingmay be wrong with the computer that produced the disagreement. If itis essential to make use ofthe computed result immediately, than likely one uses the result that two out of three computersagree on.

Next, consider software. A computer’s operating system, as well as many of its applicationprograms, contain programmingerrors. Thus, an application or operating system may “crash”unexpectedly. When I am writing a book, I have my computer system set to automatically savevarious files every few minutes. In addition, I do daily backups of my files. My computer systemis designed to attempt to recover crashed application files, and the operating system has a certainlevel of ability to detect and correct flaws that develop in the system. Is spite of all of this, fromtime to time I lose small pieces of my work.

Such crashes are only a small part of the problem when dealing with complex computerprograms that are designed to solve complex problems. An amusing example is provided by oneof the early AI medical diagnostic systems. When the system was provided input that describedarusty car, the diagnosis was measles! Other amusing examples are provided by computertranslations between natural languages.Quoting from Elaine Rich (Artificial intelligence.

NewYork: McGraw-Hill, 1984, p.341):

An idiom in the source language must berecognized and not translated directly into the targetlanguage. A classic example of the failure to do this is illustrated by the following pair ofsentences. The first was translated into Russian [by a good human translator], and the result wasthen translated back to English [by a computer], giving the second sentence:

1. The spirit is willing but the flesh is weak

2. The vodka is good but the meat is rotten.

The Websitehttp://ourworld.compuserve.com/homepages/wjhutchins/Myths.pdfsuggests thatthis may be an apocryphal story. However, the current state of the art of computer translation ofnatural languages is still quite poor.

The crux of the matter is that we are steadilyincreasing our dependence on computersystems, and use AI is of steadily increasing. I thought about this recently as I was usingcomputer software to help me do my Federal and State income tax returns. The softwarecarefully led me through a step-by-stepprocess, checked for errors, made some suggestions forhow to reduce my taxes, and produced the final forms. I have a fair level of confidence in thecalculations carried out by this tax-filing system, and the company even guarantees that thecalculationsare correct.

However, that is quite misleading. How about the logic behind the calculations? How aboutmisinterpretations of the tax law? How about my lack of understanding of what data goes wherein the overall process? I have some fears that the IRS maydecide that my tax return has measles.

To close this section, this about the idea of the possible brittleness of a person’s education.Education based on memorization without understanding is brittle. The smallest error in recallmay lead to an error in solving a problem or accomplishing a task. This is an ongoing problem inthe teaching and learning of math and in applications of math throughout the curriculum.

18

Personal Growth Activities for Chapter 2

1.

Think about memorize and regurgitate as an approach to learning. Do you often usethis approach in your own schooling? Do you use it outside of your formal schoolingenvironment? Is this a standard student approach use in the courses you teach? Doyou feel that your students make more or less use of thisapproach, as compared tothe students of your fellow teachers? After you have reflected on memorization andregurgitation, discuss the topic with your colleagues and your students. Your goal isto gain increased insight into how they feel about this approach to “learning.”

2.

Make up your own, personal definition of lower-order and higher-order knowledgeand skills. Illustrate using examples form your own personal knowledge and skills.

Activities for Chapter 2

1.

The diagram given below is a combination ofseveral diagrams given in this chapter.Select some area in which you have a high level of expertise. Using the variouscomponents of this diagram, analyze your expertise and how you acquired thisexpertise.

Repeat Activity 1 for an area in which you have a medium level (a useful level) ofexpertise.

3.

Select an area where you currently have a novice level of expertise. Using the diagramfrom Activity 1, along with your insights into your personal learning characteristics,analyze what would best help you to move up the expertise scale.

4.

The word “understanding” is used throughout this chapter, but is not defined in thechapter. What is your personal understanding of the meaning of understanding? Notethat in developing lesson plans, some teachers make frequent use of the term, while

19

others carefully avoid using it. What are your thoughts on this? How can one readilyassess a student’s level of understanding of a topic that you are teaching.

5.

Compare and contrast “Acquisition and Retention” from a human-as-learner and acomputer-as-learner points of view. Earlier in this chapter we noted that memorizeand regurgitate, with little or no understanding, is often considered a useful approachto solving the problem of getting a good grade on a test. That is, there are certain kindsof problem-solving situations in which rote memory is quite useful. Computersystems can have very large rote memories that can be designed so that the memorized(that is, stored) material is retained for days, week, months, or years. Thus, yourcompare/contrast analysis should include your insights into the value of this type oflearning for people and for machines.

6.

Memorize, regurgitate, and forget is useful outside of the formal school setting. Forexample, you are at a meeting or a party and you are introduced to a large number ofpeople you don’t know. It is helpful to quickly memorize names and to make use ofthe names during the meeting or party. People vary greatly in their ability to do this,and their ability to remember the names when meeting the people at a later date. Givesome other examples of this sort of learning outside of a school setting. Analyze thesituation from a personal point of view and from the point of view and from the pointof view of possible uses of computer technology. (Someday not too far in the futurepeople will have eye glasses with a built in video camera and face recognition system.The system will recognize faces and speak the names into a very small “hearing aid”that a person is wearing.)

20

Chapter 3: Computer Chess and Chesslandia

In Minsky’s interview given in chapter 1, he noted that is much easier to program a computerto play chessthan it is to develop a computerized robot that can do routine household work. Still,developing a computer program with a high level of chess expertise has proven to be achallenging AI task (Games & Puzzles, n.d.). This chapter explores this effort and some of itseducational implications. In addition, it introduces Alan Turing and the Turing Test for computerintelligence.

Alan Turing and the Turing Test

Alan Turing (1912-1954) was a very good mathematician and a pioneer in the field ofelectronic digital computers. In 1936, he published a math paper that provides theoreticalunderpinnings for the capabilities and limitations of computers. During World War II, he helpeddevelop computers in England that played a significant role in England’s war efforts. In 1950,Alan Turing published a paper discussing ideas of current and potential computer intelligence,and describing what is now known as the Turing Test for AI (Turing, 1950).

TheTuring Test is an imitation game. A person in the first of three isolated rooms has twocomputer terminals. One terminal is directly connected to a terminal being run by a secondperson, who is located in a second room. The other terminal is directly connected to a computer,located in a third room. The computer has been programmed to be able to carry on a writtenconversation via its terminal, imitating the written conversational capabilities of a human.

The first person carries on two written conversations (via terminals) with the second personand the computer, without knowing which is which. The first person’s goal is to determine whichwritten conversation is being carried out with a person, and which with a computer. Turing’s1950 paper predicted thatby the year 2000 there would be computers that routinely fooledhumans in this imitation game task.

Interestingly, the field of AI has not yet passed Turing’s Test. A prize has been establishedand from time to time contests are held to see if a computerprogram has been developed that canpass the test (Loebner Prize, n.d.). At the current time, humans are far better than computers atcarrying on a written conversation. Moreover, humans are still better at carrying on an oralconversation, far exceeding computers in this task. In both written and oral conversations,humans are far far better than computers at understanding the conversation.

Emergence of the Electronic Digital Computer Industry

Up until 1950, each electronic digital computer that was constructed was a “one of a kind”machine. By 1950, about 20 computers had been built. Technological progress in this field wasso rapid that by the time a machine was completed it was nearly obsolete. The demand forcomputers was quite low. Here is a now-amusing quotation that represented an early estimate ofthe potential market demand for computers.

I think there is a world market for maybe five computers.

(Thomas Watson, chairman of IBM, 1943.)

Thomas Watson not withstanding, by 1950 it was clear that there was a rapidly growingmarket for computers. The first mass-produced computer in the United States was the UNIVACI, first produced in 1951. The following quotation indicatesthe speed of this machine as well asthe fact that only 46 were sold over a period of about six years.

21

The UNIVAC I (the name stood for Universal Automatic Computer) was delivered to the [UnitedStates] Census Bureau in 1951. It weighed some 16,000 pounds, used 5,000 vacuum tubes, andcould perform about 1,000 calculations per second. It was the first American commercialcomputer, as well as the first computer designed for business use. (Business computers like theUNIVAC processed data more slowly than the IAS-type machines, but were designed for fastinput and output.) The first few sales were to government agencies, the A.C. Nielsen Company,and the Prudential Insurance Company. The first UNIVAC for business applications was installedat the General Electric Appliance Division, to do payroll, in 1954. By 1957 Remington-Rand(which had purchased the Eckert-Mauchly Computer Corporation in 1950) had sold forty-sixmachines. (UNIVAC)

Note that a modern laptop computer is about a million times as fast as the UNIVAC I, costsless than 1/2,000 as much (taking into consideration inflation), and weighs less than 1/2,000 asmuch. Raw speed, cost, and portability are important parts of an ICT system’s capabilities. Notealso that the early computers lacked connectivity (the Internet, along with email and the Web,did not exist) and did not have the applications such as word processor, spreadsheet, draw andpaint graphics, database, and so on that we now take for granted.

Early electronic digital computers were often referred to as “electronic brains.” As electronicdigital computers became increasingly available in the late 1940s and early 1950s, a smallnumber of people began to think about the possibility of developing a computer program thatcould play the game of chess. Since chess is an intellectual game, a chess-playing computerprogram would be a good demonstration of the brain-like capabilities of computers.

Computer Chess

Here is a brief chronology of some early aspects of computer chess (Wall, n.d.).

•

In 1947, Alan Turing specified (in a conceptual manner) the first chess program forchess.

•

In 1949 Claude Shannon described how to program a computer to play chess, and aFerranti digital machine was programmed to solve mates in two moves. He proposedbasic strategies for restricting the number of possibilities to be considered in a game ofchess.

•

In 1950, Alan Turing wrote the first computer chess program.

•

By 1956, experiments on a MANIAC I computer (11,000 operations a second) at LosAlamos, using a 6x6 chessboard, was playing chess. This was the first documentedaccount of a running chess program.

•

In 1957 a chess program was written by Bernstein for an IBM 704. This was the first full-fledged game of chess by a computer.

•

In 1958, a chess program beat a human player for the first time (a secretary who wastaught how to play chess just before the game).

The last item on the list is particularly interesting. The secretary had received about one hourof instruction on how to play chess. The computer displayed a level of chess-playing expertisegreater than a human could gain through one hour of individualized instruction. Thus, we havesome of the first inklings of a tradeoff between human learning time and replacing this time andeffort by an “intelligent” machine.

The early game-playing computer systems were of rather limited capability. In no sense werethey able to challenge a human player with even moderate capability. However, over the years,

22

more powerful computers were developed, and progress occurred in the underlying theory andpractice of game-playing programs.

Slow but steady progress in computer chess playing has continued over the years.Tournaments were established so that computers could compete against other computers.Demonstrations were held, pitting human players against computers. Eventually computers wereallowed to compete in some human chess tournaments.

Computer chess programs got better and better through a combination of greater computerspeed and better programming. In May 1997, IBM's Deep Blue supercomputer played afascinating match with the reigning World Chess Champion, Garry Kasparov. AlthoughKasparov was considered to be one of the strongest chess players of all time and the match wasclose, the computer won (Deep Blue, n.d.).

In early 2003, a six game match was played between Garry Kasparov and Deep Junior, thecurrent reigning world computer chess champion. Deep Blue had long since “retired”. DeepJunior used a much slower computer than Deep Blue, but it employed much more sophisticated“intelligence” in its programming.

The computer that Deep Junior was running on was only 1/66 as fast as that used by DeepBlue. And, Kasporov was no longer the reigning human world chess champion. The six gamematch ended in a draw, with one victory for each player, and four tied games (Deep Junior, n.d.-).

Nowadays one can buy avariety of relatively good game-playing programs that run on amicrocomputer. Quite likely such programs can easily beat you at chess, checkers, backgammon,bridge, and a variety of other games.

The message is clear. In the narrow confines of games and relatively similar real-worldproblem solving, computers now have a relatively high level of expertise. In some of thesegames, computer expertise now exceeds the highest level of human expertise.

Chesslandia

The educational implications of such computer expertise are quite interesting. The followingis an editorial (still one of my favorites) that I wrote in 1987.

Moursund, D.G. (March 1987). Chesslandia: A parable.Learning and Leading with Technology.Accessed 4/23/06:http://darkwing.uoregon.edu/~moursund/dave/LLT-Eds/LLT-V14-1986-87.html#LLTV14%236.

Chesslandia: A Parable

Chesslandia was aptly named. In Chesslandia, almost everybody played chess. A child's earliesttoys were chess pieces, chess boards, and figurines of famous chess masters. Children's bedtimetales focused on historical chess games and on great chess-playing folk heroes . Many of thechildren's television adventure programs were woven around a theme of chess strategy. Mostadults watched chess matches on evening and weekend television.

Language was rich in chess vocabulary and metaphors. "I felt powerless--like a pawn facing aqueen." "I sent her flowers as an opening gambit." "His methodical, breadth-first approach toproblem solving does not suit him to be a player in our company." "I lacked mobility--I had nochoice."

The reason was simple. Citizens of Chesslandia had to cope with the deadly CHESS MONSTER!The CHESS MONSTER, usually just called the CM, was large, strong, and fast. It had a voraciousappetite for citizens of Chesslandia, although it could survive on a mixed diet of vegetation andsmall animals.

23

The CM was a wild animal in every respect but one. It was born with an ability to play chess andan innate desire to play the game. A CM's highest form of pleasure was to defeat a citizen ofChesslandia at a game of chess, and then to eat the defeated victim. Sometimes a CM would sparea defeated victim if the gamewas well played, perhaps savoring a future match.

In Chesslandia, young children were always accompanied by adults when they went outside. Onecould never tell when a CM might appear. The adult carried several portable chess boards. (WhileCMs usually traveled alone, sometimes a group traveled together. Citizens who were adept atplaying several simultaneous chess games had a better chance of survival.)

Formal education for adulthood survival in Chesslandia began in the first grade. Indeed, inkindergartenchildren learned to draw pictures of chess boards and chess pieces. Many childrenlearned how each piece moves even before entering kindergarten. Nursery rhyme songs andchildren's games helped this memorization process.

In the first grade, students wereexpected to master the rudiments of chess. They learned to set upthe board, name the pieces, make each of the legal moves, and tell when a game had ended.Students learned chess notation so they could record their moves and begin to read chess books.Reading was taught from the "Dick and Jane Chess Series." Even first graders played importantroles in the school play, presented at the end of each year. The play was about a famous chessmaster and contained the immortal lines: "To castle or not to castle--that is the question."

In the second grade, students began studying chess openings. The goal was to memorize thedetails of the 1,000 most important openings before finishing high school. A spiral curriculum hadbeen developed over the years. Certain key chess ideas were introduced at each grade level, andthen reviewed and studied in more depth each subsequent year.

As might be expected, some children had more natural chess talent than others. By the end of thethird grade, some students were a full two years behind grade level. Such chess illiteracy caughtthe eyes of the nation, so soon there were massive, federally-funded remediation programs. Therewere also gifted and talented programs for students who were particularly adept at learning chess.One especially noteworthy program taught fourth grade gifted and talented students to playblindfold chess. (Although CMs were not nocturnal creatures, they were sometimes still outhunting at dusk. Besides, a solar eclipse could lead to darkness during the day.)

Some students just could not learn to play a decent game of chess, remaining chess illiterate nomatter how many years they went to school. This necessitated lifelong supervision in institutionsor shelter homes. For years there was a major controversy asto whether these students shouldattend special schools or be integrated into the regular school system. Surprisingly, when thisintegration was mandated by law, many of these students did quite well in subjects not requiring adeep mastery of chess. However, such subjects were considered to have little academic merit.

The secondary school curriculum allowed for specialization. Students could focus on the worldhistory of chess, or they could study the chess history of their own country. One high school builta course around the chess history of its community, with students digging into historical recordsand interviewing people in a retirement home.

Students in mathematics courses studied breadth-first versus depth-first algorithms, boardevaluation functions, and the underlying mathematical theory of chess. A book titled "AMathematical Analysis of some Roles of Center Control in Mobility." was often used as a text inthe advanced placement course for students intending to go on to college.

Some schools offered a psychology course with a theme on how to psych out an opponent. Thiscourse was controversial, because there was little evidence one could psych out a CM. However,proponents of the course claimed it was also applicable to business and other areas.

Students of dance and drama learned to represent chess pieces, their movement, the flow of agame, the interplay of pieces, and the beauty of a well-played match. But such studies weredeemed to carry little weight toward getting into the better colleges.

All of this was, course, long long ago. All contact with Chesslandia has been lost for many years.

That is, of course, another story. We know its beginning. The Chesslandia government andindustry supported a massive educational research and development program. Of course, the mainbody of research funds was devoted to facilitating progress in the theory and pedagogy of chess.

Quite early on it became evident that a computer could be programmed to play chess. But, it wasargued, this would be of little practical value. Computers could never play as well as adultcitizens. And besides, computers were very large, expensive, and hard to learn to use. Thus,educational research funds for computer-chess were severely restricted.

However, over a period of years computers got faster, cheaper, smaller, and easier to use. Betterand better chess programs were developed. Eventually, portable chess-playing computers were

developed, and these machines could play better than most adult citizens. Laboratory experimentswere conducted, using CMs from zoos, to see what happened when these machines were pittedagainst CMs. It soon became evident that portable chess-machines could easily defeat most CMs.

While educators were slow to understand the deeper implications of chess-playing computers,many soon decided that the machines could be used in schools. "Students can practice against thechess-machine. The machine can be set toplay at an appropriate level, it can keep detailed recordsof each game, and it has infinite patience." Parents called for "chess-machine literacy" to beincluded in the curriculum. Several state legislatures passed requirements that all students in their

schools must pass a chess-machine literacy test.

At the same time, a few educational philosophers began to question the merits of the currentcurricula, even those which included a chess-computer literacy course. Why should the curriculumspend so much time teaching students to play chess? Why not just equip each student with a chess-machine, and revise the curriculum so it focuses on other topics?

There was a call for educational reform, especially from people who had a substantial knowledgeof how to usecomputers to play chess and to help solve other types of problems. Opposition frommost educators and parents was strong. "A chess-machine cannot and will never think like an adultcitizen. Moreover, there are a few CMs that can defeat the best chess-machine. Besides, one cannever tell when the batteries in the chess-machine might wear out." A third grade teacher notedthat "I teach students the end game. What will I do if I don't teach students to deal with the endgame?" Other leading citizens and educators noted that chess was much more than a game. It wasa language, a culture, a value system, a way of deciding who will get into the better colleges or getthe better jobs.

Many parents and educators were confused. They wanted the best possible educationfor theirchildren. Many felt that the discipline of learning to play chess was essential to successfuladulthood. "I would never want to become dependent on a machine. I remember having tomemorize three different chess openings each week. And I rememberthe worksheets that we hadto do each night, practicing these openings over and over. I feel that this type of homework buildscharacter."

The education riots began soon thereafter.

The intended message of this editorial is that we need to carefully examine our educationsystem, looking for places where we are currently teaching students to do things that machinescan do well. The general idea present here is by no means new. See Peddiwell (1939) for asimilar essay written before the development of electronic digital computers.

Tools can be mass produced and mass distributed. The education of students is, in essence,still a craft industry. Although our educational system has certain mass production, factory-likecharacteristics, learning is still an individual thing. Thus, we need to think very carefully abouthow to best use a student’s learning capabilities and time. As suggested by the Chesslandiaparable, there is potential peril in spending too much time and effort educating students tocompete with machines!

Personal Growth Activities for Chapter 3

1.

Share the Chesslandia parable with a friend. Then carry on a conversation that looksfor parallels between this parable and certain aspects of our current educational

25

system. One of the problems of our current curriculum is that it is “full.” Throughsuch conversations, you may begin to identify parts of the current curriculum that arebecoming increasingly unnecessary through changes in technology and our society.

2.

Repeat Personal Growth Activity 1, but with some students. Your goal is to achieveincreased insight into what aspects of the curriculum they feel is worthwhile, andwhat aspects they feel might be deleted.

Activities for Chapter 3

1.

You have grown up with the idea that a car is faster than a person, an airplane is fasterthan a car, and a spaceship is faster than an airplane. Although Superman is “morepowerful than a locomotive and faster than a speeding bullet,” you know thatordinary people lack these capabilities. Explore your feelings and insights into the factthat a computer can play chess, checkers, backgammon, and a number of other gamesbetter than you. As you do this, compare and contrast with your feelings about cars,airplanes, and locomotives.

2.

Historically, “having a good hand” (referring to neat penmanship) was considered asign of a good education. Even the earliest typewriters made it possible for a personto learn to write faster and neater than by hand. A word processor is a still morepowerful aid to “having a good hand.” Discuss your feelings about schools spendingtime and effort on children developing good (by hand) penmanship versus havingstudents learn to use a word processor. Do not couch your discussion in an either-orform. We might want students to learn to print legibly and use a word processor well.

3.

Select a cognitive skill-based game in which you have a reasonably good level ofexpertise. Make a rough estimate of the number of hours it took you to achieve thislevel of expertise. Then give some arguments that this was a good use of your time,independently of whether a computer can play the game better than you. (Forexample, perhaps the game time was an important part of developing social skills andfriends.)

4.

This is a follow-up to (3) above. Discuss transfer of learning (your knowledge andskill) from the game you analyzed in (3) to real world problem-solving situations.Focus specifically on the nature and extent of transfer of your game playingknowledge and skills.

26

Chapter 4: Algorithmic and Heuristic Procedures

In this chapter, we use the termprocedureto refer to a detailed set of instructions that can becarried out by a specified agent such as automated factory machinery, a computer, or a person.This chapter provides background information needed as we explore “intelligent-like”procedures that can be carried out by computers.

Procedure

At some time in your life, you learned and/or memorized procedures for multi-digitmultiplication and long division, looking up a word in a dictionary or a name in a telephonebook, alphabetizing a list, and to accomplish many other routine tasks.

In this bookt, we use the definition:a procedureis a detailed step-by-step set of directionsthat can be interpreted and carried out by a specified agent. Our focus is on procedures designedto solve or help solve a specified category of problems. Remember, our definition ofproblem

includes accomplishing tasks, making decisions, answering questions, and so on. We areparticularly interested in procedures that humans can carry out and in procedures that computerscan carry out. Figure 4.1 is designed to illustrate the overlap between procedures that ICTsystems can carry out and procedures that humans can carry out.

ICTProceduresHumanProcedures

Figure 4.1. Procedures to be carried out by ICT systems and by humans.

In this chapter, we explore two types of procedures:

1.

Algorithm. An algorithm is a procedure thatis guaranteedto solve the problem oraccomplish the task for which it is designed. You know a paper and pencil algorithmfor multiplying multi-digit numbers. If you carry out the procedure (the algorithm)without error, you will solve the multiplication problem.

2.

Heuristic. A heuristic is a procedure that is designed to solve a problem or accomplisha task, but thatis not guaranteedto solve the problem or accomplish the task. Aheuristic is often called a rule of thumb. You know and routinely use lots of heuristics.They work successfully often enough for you so that you continue to use them. Forexample, perhaps you have a heuristic that guides your actions as you try to avoidtraffic jams or try to find a parking place. Perhaps you use heuristics to help preparefor a test or for making friends. Teachers make use of a variety of heuristics forclassroom management.

The following quotation from Marvin Minsky (1960) indicates that early researchers in AIhad a good understanding of the roles of heuristic programming in AI.

27

The problems of heuristic programming—of making computers solve really difficult problems—are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction.

…

The adjective "heuristic," as used here and widely in the literature, means related to improvingproblem-solving performance; as a noun it is also used in regard to any method or trick used toimprove the efficiency of a problem-solving system.A "heuristic program," to be consideredsuccessful, must work well on a variety of problems, and may often be excused if it fails onsome.We often find it worthwhile to introduce a heuristic method, which happens to causeoccasional failures, if there is an over-all improvement in performance. [Bold added for emphasis.]

ICT systems are very fast and accurate at carrying out algorithms. A mid-pricedmicrocomputer can carry out more than a billion arithmetic computations per second. This isdone without errors, following algorithms built into its circuitry. Computers can look up a wordin a dictionary or alphabetize a list of names because programs have been written to carry outalgorithms designed to accomplish these tasks.

AI programs make use of both algorithmic and heuristic procedures. Many of the problemsbeing addressed using AI have the two characteristics:

1.

The problems are very difficult from ahuman point of view, and a human world-classexpert does not solve them perfectly. Indeed, many of the problems are beyondhuman (unaided by computers) capabilities.

2.

The computer programs being written to address these problems make use ofalgorithms, heuristics, computer speed, computer storage capacity, and computerconnectivity. This approach can produce ICT systems of increasing expertise withinmany different problem-solving areas of interest to people, but cannot guaranteesuccess in solving all ofthe problems within each of these areas.

Think about the challenges of writing a computer program that can pass the Turing Test—that can carry on a good written conversation with a person. Many people have written computerprograms that can carry on a written conversation via a computer terminal. Perhaps the most wellknown is a program named ELIZA written by Joseph Weizenbaum and published in 1966(Weizenbaum). This heuristic program indeed carries on a written conversation. From time totime people using the program have actually believed they were conversing with another humanbeing. However, the program is not nearly good enough to pass the Turing Test. The ELIZAprogram is available on the Web and many peoplefind that it is fun to play with this program(ELIZA).

The following “conversation is quoted from Weizenbaum (1966). The capitalized text waswritten by the computer in response to the non-capitalized text provided by a human.

Men are all alike.

IN WHAT WAY?

They're always bugging us about something or other.

CAN YOU THINK OF A SPECIFIC EXAMPLE?

Well, my boyfriend made me come here.

YOUR BOYFRIEND MADE YOU COME HERE

He says I'm depressed much of the time.

28

I AM SORRY TO HEAR YOU ARE DEPRESSED

It's true. I am unhappy

DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY

I need some help, that much seems certain.

WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP

Perhaps I could learn to get along with my mother.

TELL ME MORE ABOUT YOUR FAMILY

My mother takes care of me.

WHO ELSE IN YOUR FAMILY TAKES CARE OF YOU

My father.

YOUR FATHER

You are like my father in some ways.

WHAT RESEMBLANCE DO YOU SEE

You are not very aggressive but I think you don't want me to notice that.

WHAT MAKESYOU THINK I AM NOT VERY AGGRESSIVE

You don't argue with me.

WHY DO YOU THINK I DON'T ARGUE WITH YOU

You are afraid of me.

DOES IT PLEASE YOU TO BELIEVE I AM AFRAID OF YOU

My father is afraid of everybody.

WHAT ELSE COMES TO YOUR MIND WHEN YOU THINK OF YOUR FATHER

Bullies.

DOES THAT HAVE ANYTHING TO DO WITH THE FACT THAT YOUR BOYFRIENDMADE YOU COME HERE

The conversation rambles on in a rather pointless manner. The ELIZA program representsreally clever thinking and programming for the time whenit was written. You might enjoycarrying on a conversation with a more modern program. My students have enjoyedexperimenting with IntelliBuddy (n.d.).

Handheld Calculators

In the remainder of this chapter, we will explore the capabilities of simple handheld mathcalculators. These calculators make use of algorithms. Our goal is to help you gain increasedinsight into what mightbe calledalgorithmic intelligence. People vary considerably in their

29

ability to memorize an algorithm and carry it out rapidly and accurately. That is, people varyconsiderably in their algorithmic intelligence. With appropriate education, training, andexperience, a person can increase his or her algorithmic intelligence.

Here, the term “intelligence” is used very loosely. If we think in terms of fluid andcrystallized intelligence (gF and gI), then we can talk about innate intelligence related to learningalgorithms versus one’s accumulated algorithmic knowledge and skills. (Learn more aboutintelligence in Moursund, (2006, Chapter 2).) In any case, keep in mind that the “intelligence” ofa handheld calculator designed to perform arithmetic calculationsis a lot different than the typeof intelligence that a person has. However, a person can be educated/trained to be relatively goodat doing what a 4-function calculator can do.

A significant part of our current educational curriculum is devoted to helping studentsmemorize algorithmic procedures and to develop speed and accuracy in carrying out these